library(Hmisc)
library(tidyverse)Homework 1
Load Packages
Problem 1
Survey
August 29, 9:20 pm EST
Campuswire
Problem 2
Question 1
The population of data set 1 consists of individuals aged 16 or older who are not living in communal residences in England and Wales. The population of data set 2 consists of all crimes recorded and investigated by UK police forces.
Question 2
Data set 1 has a voluntary response as the strategy because the answers are self-reported. Data set 2 uses convenience as their strategy.
Question 3
Data set 1’s population is 38,000 people. Data set 2’s sampled population is the records
Question 4
data set 1’s target population is UK residence
Question 5
Data set 1 has somewhat reliability because it’s self reported and people could have lied, but reliability for data set 2 is reliable because it is records. The validity of data set 1 is good due to the large number of people and large age range. Demographic is good. Data set 2 also has good validity because it’s records. Data set 1’s study population is generalizable to the target population because of the large age range and amount of people included in the population. Data set 2 is also generalizable to the target population.
Problem 3
Question 1
The <- notation is equivalent to an = sign in R and is often used to declare variables. After running this code chunk, the named dataframe df appears in the environment on the right-hand side of RStudio.
df <- read_csv('https://www.openintro.org/data/csv/babies.csv')Rows: 1236 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): case, bwt, gestation, parity, age, height, weight, smoke
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Question 2
The notation Hmisc:: directly calls this function from the Hmisc package. describe() is a common function name, and sometimes this is needed to indicate to R which function from which package you want to use. The pipe feature |> sends the results of the first line directly into the function on the 2nd line and is a convenient way to chain functions together.
This code prints a useful and attractive summary of the data set we are using.
Hmisc::describe(df) |>
html()8 Variables 1236 Observations
case
n missing distinct Info Mean Gmd .05 .10 .25
1236 0 1236 1 618.5 412.3 62.75 124.50 309.75
.50 .75 .90 .95
618.50 927.25 1112.50 1174.25
lowest : 1 2 3 4 5 , highest: 1232 1233 1234 1235 1236
bwt
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1236 | 0 | 107 | 1 | 119.6 | 20.33 | 88.0 | 97.0 | 108.8 | 120.0 | 131.0 | 142.0 | 149.0 |
gestation
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1223 | 13 | 106 | 0.999 | 279.3 | 16.57 | 252.0 | 262.0 | 272.0 | 280.0 | 288.0 | 295.8 | 302.0 |
parity
| n | missing | distinct | Info | Sum | Mean | Gmd |
|---|---|---|---|---|---|---|
| 1236 | 0 | 2 | 0.57 | 315 | 0.2549 | 0.3801 |
age
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1234 | 2 | 30 | 0.997 | 27.26 | 6.506 | 19 | 20 | 23 | 26 | 31 | 36 | 38 |
height
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1214 | 22 | 19 | 0.986 | 64.05 | 2.839 | 60 | 61 | 62 | 64 | 66 | 67 | 68 |
Value 53 54 56 57 58 59 60 61 62 63 64 65
Frequency 1 1 1 1 10 26 55 105 131 166 183 182
Proportion 0.001 0.001 0.001 0.001 0.008 0.021 0.045 0.086 0.108 0.137 0.151 0.150
Value 66 67 68 69 70 71 72
Frequency 153 105 54 20 13 6 1
Proportion 0.126 0.086 0.044 0.016 0.011 0.005 0.001
For the frequency table, variable is rounded to the nearest 0
weight
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1200 | 36 | 105 | 0.999 | 128.6 | 22.39 | 102.0 | 105.0 | 114.8 | 125.0 | 139.0 | 155.0 | 170.0 |
smoke
| n | missing | distinct | Info | Sum | Mean | Gmd |
|---|---|---|---|---|---|---|
| 1226 | 10 | 2 | 0.717 | 484 | 0.3948 | 0.4782 |
Question 3
The Child Health and Development Studies investigate a range of topics. One study, in particular, considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. The variables in this data set are as follows.
| Variable Name | Variable Description | Variable Type |
|---|---|---|
case |
id number | categorical, multicategorical |
bwt |
birthweight, in ounces | numerical |
gestation |
length of gestation, in days | numerical |
parity |
binary indicator for a first pregnancy (0 = first pregnancy) | categorical, binary |
age |
mother’s age in years | numerical |
height |
mother’s height in inches | numerical |
weight |
mother’s weight in pounds | numerical |
smoke |
binary indicator for whether the mother smokes | categorical, binary |
Question 4
Below, 2 numeric variables were investigated for potential relationships. The independent, explanatory variable I chose is variable_name, and the dependent, response variable I chose is variable_name.
df |>
ggplot(aes(x = gestation, # please change these
y = bwt)) +
geom_point()Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).
The highest birth weight is 100-150 between the gestation periods of 250-300
Session Info
This portion of the document describes the conditions in RStudio under which this report was created. This is important to include so that work is reproducible by others.
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[9] ggplot2_3.5.1 tidyverse_2.0.0 Hmisc_5.1-1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 xfun_0.47 colorspace_2.1-1 vctrs_0.6.5
[5] generics_0.1.3 htmltools_0.5.8.1 yaml_2.3.10 base64enc_0.1-3
[9] utf8_1.2.4 rlang_1.1.4 pillar_1.9.0 foreign_0.8-87
[13] glue_1.7.0 withr_3.0.1 bit64_4.0.5 lifecycle_1.0.4
[17] munsell_0.5.1 gtable_0.3.5 htmlwidgets_1.6.4 evaluate_0.24.0
[21] labeling_0.4.3 knitr_1.48 tzdb_0.4.0 fastmap_1.2.0
[25] curl_5.2.2 parallel_4.2.1 fansi_1.0.6 htmlTable_2.4.3
[29] scales_1.3.0 backports_1.5.0 checkmate_2.3.2 vroom_1.6.5
[33] jsonlite_1.8.8 farver_2.1.2 bit_4.0.5 gridExtra_2.3
[37] hms_1.1.3 digest_0.6.37 stringi_1.8.4 grid_4.2.1
[41] cli_3.6.3 tools_4.2.1 magrittr_2.0.3 Formula_1.2-5
[45] cluster_2.1.6 crayon_1.5.3 pkgconfig_2.0.3 data.table_1.16.0
[49] timechange_0.3.0 rmarkdown_2.28 rstudioapi_0.16.0 R6_2.5.1
[53] rpart_4.1.23 nnet_7.3-19 compiler_4.2.1